Figures for manuscript
Jonathan Harrison
14/09/2018
Outline of Figures:
Fig1: Drosophila egg chambers exhibit a characteristic network structure of connections between nurse cells A) Cartoon of tissue, B) Equivalent image of tissue, C) Diagram of connections between NCs via RCs. 
Fig2: Behaviour of differential equation model. A) Dynamics in time shown for each cell, B) Model related to network structure of nurse cells.
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Fig3: Assembly of higher order complexes in oocyte A) Particles in different regions of the egg chamber, B) Distribution of intensities of particles in different regions.
## # A tibble: 3 x 5
## phenotype av std av_median stnd_error
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 OE 0.508 0.608 0.300 0.0287
## 2 UE 1.62 1.63 1.03 0.122
## 3 WT 0.563 0.702 0.345 0.0380
Fig4: Characterisation of the bias in directionality of transport through ring canals A) Schematic of transport between two compartments, B) Posterior pairs plot, C) Posterior predictive plot. 
## purple
## 1 #e5cce5
## 2 #bf7fbf
## 3 #a64ca6
## 4 #800080
## 5 #660066
## 6 #400040
Fig5: Results of inference for dynamic model A) Posterior pairs plot, B) Posterior predictive distribution, C) Sensitivity to a, b.
## [1] "using real data \n"
## Inference for Stan model: mrna_transport_no_decay.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
## a 13.24 0.06 2.57 9.48 11.45 12.90 14.56 18.92 1630 1
## b 0.24 0.00 0.05 0.16 0.21 0.24 0.27 0.35 1800 1
## nu 0.96 0.00 0.04 0.86 0.94 0.97 0.98 1.00 2695 1
## phi 0.33 0.00 0.04 0.26 0.30 0.33 0.35 0.39 2411 1
## sigma 1.21 0.00 0.13 0.98 1.12 1.20 1.30 1.48 2718 1
##
## Samples were drawn using NUTS(diag_e) at Fri Sep 14 17:42:45 2018.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).
## [1] "using real data \n"
## [1] "swapped"
## [1] 0.00001 2.40820 12.43159 14.79800 19.54926 21.98536 22.69240 22.81189
## [9] 24.29904
## [1] 9
## Inference for Stan model: mrna_transport_no_decay.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
## a 32.32 0.10 6.48 20.45 27.84 32.17 36.44 45.77 4000 1
## b 109.12 0.11 7.01 95.56 104.32 109.06 113.84 122.95 4000 1
## nu 0.56 0.00 0.04 0.47 0.53 0.56 0.59 0.64 3033 1
## phi 0.57 0.00 0.03 0.51 0.55 0.57 0.59 0.63 4000 1
## sigma 0.02 0.00 0.00 0.02 0.02 0.02 0.03 0.03 4000 1
##
## Samples were drawn using NUTS(diag_e) at Tue Sep 18 20:28:11 2018.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).
optional caption text
Fig7: Validation of testable prediction for over expression versus measured results.
FigS1: (Supplementary) Exponential growth model to establish timescales A) Schematic of time points used for each stage, B) Linear regression of log(A) against the time points for each stage. 
## $t0
## # A tibble: 3 x 2
## split estimate
## <fct> <dbl>
## 1 Overexpression 6.90
## 2 test 7.86
## 3 train 7.57
##
## $ts1
## [1] 14.62700 17.34324 17.97174 23.47762 23.55478 25.04558 25.92174 30.41032
## [9] 32.14798
##
## $ts2
## [1] 0.00001 2.40820 11.31019 12.43159 14.62700 14.79800 17.28339
## [8] 17.34324 17.97174 19.54926 19.66385 20.77397 20.90405 21.60169
## [15] 21.78633 21.90111 21.98536 22.69240 22.81189 23.47762 23.55478
## [22] 24.29904 25.04558 25.26811 25.92174 26.88113 26.96548 30.41032
## [29] 32.14798
##
## $ts3
## [1] 0.00001 2.40820 11.31019 12.43159 14.79800 17.28339 19.54926
## [8] 19.66385 20.77397 20.90405 21.60169 21.78633 21.90111 21.98536
## [15] 22.69240 22.81189 24.29904 25.26811 26.88113 26.96548
##
## $ts4
## [1] 0.00001 2.40820 12.43159 14.79800 19.54926 21.98536 22.69240 22.81189
## [9] 24.29904
##
## $sort_indices1
## [1] 2 4 8 3 7 9 1 5 6
##
## $sort_indices2
## [1] 28 22 10 24 2 26 17 4 8 27 14 15 12 18 11 20 25 21 23 3 7 29 9
## [24] 19 1 13 16 5 6
##
## $sort_indices3
## [1] 1 8 5 6 3 9 2 11 10 4 7
##
## $sort_indices4
## [1] 8 2 4 6 7 5 1 3 9
##
## $time_scaling
## # A tibble: 3 x 2
## split estimate
## <fct> <dbl>
## 1 Overexpression 0.0715
## 2 test 0.0425
## 3 train 0.0559FigS2: Convergence diagnostics for MCMC inference methods
FigS3: Prior predictive distribution
## [1] "using real data \n"
## [1] "Using the following stan file: prior_predictive_no_decay.stan"
## Inference for Stan model: prior_predictive_no_decay.
## 4 chains, each with iter=2000; warmup=1000; thin=1;
## post-warmup draws per chain=1000, total post-warmup draws=4000.
##
## mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
## theta[1] 7.88 0.09 5.98 0.29 3.15 6.59 11.32 22.32 4000 1
## theta[2] 8.03 0.10 6.15 0.27 3.09 6.75 11.66 22.95 4000 1
## theta[3] 0.50 0.00 0.29 0.02 0.25 0.50 0.74 0.97 4000 1
## phi 2.97 0.02 0.99 1.09 2.29 2.96 3.63 4.98 3664 1
## sigma 0.79 0.01 0.60 0.03 0.31 0.66 1.15 2.22 3998 1
## a 8.03 0.10 6.15 0.27 3.09 6.75 11.66 22.95 4000 1
## b 7.88 0.09 5.98 0.29 3.15 6.59 11.32 22.32 4000 1
##
## Samples were drawn using NUTS(diag_e) at Thu Sep 27 16:45:34 2018.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).
FigSPairs: Posterior pairwise plot